Methods, systems and computer program products for optical coherence tomography (OCT) using automatic dispersion compensation

ABSTRACT

Methods, systems and computer program products for generating parameters for software dispersion compensation in optical coherence tomography (OCT) systems are provided. Raw spectral interferogram data is acquired for a given lateral position on a sample and a given reference reflection. A trial spectral phase corresponding to each wavenumber sample of the acquired spectral interferogram data is postulated. The acquired raw spectral data and the postulated trial spectral phase data are assembled into trial complex spectrum data. Trial A-scan data is computed by performing an inverse Fourier transform on the trial complex spectrum data and determining the magnitude of a result.

CROSS-REFERENCE TO RELATED APPLICATION

The present application claims priority from U.S. ProvisionalApplication No. 60/795,790 (Attorney Docket No. 9526-11PR), filed Apr.28, 2006, the disclosure of which is hereby incorporated herein byreference as if set forth in its entirety.

FIELD OF THE INVENTION

The present invention relates to imaging and, more particularly, tooptical coherence tomography (OCT) and related methods, systems andcomputer program products.

BACKGROUND OF THE INVENTION

Optical Coherence Tomography (OCT) is technique for imaging intosamples, such as tissue, glass and the like. Recent advances in OCTinclude Fourier Domain (FD)-OCT, in which either a broadband source witha spectrometer (SD-OCT) or a swept laser source with a single photodiode(SS-OCT) is used to generate OCT images. These OCT architectures may bedependent on the dispersion properties of a reference arm and a samplearm, particularly the relative variation in dispersion between the twoarms. This variation in dispersion can be compensated either by changingthe optical properties of the sample or reference arm or by usingnumerical compensation techniques in the OCT processing and imagingsoftware. This numerical compensation may require one or more parametersfor the dispersion management.

Referring now to FIG. 1, a schematic block diagram illustrating aconventional technique for dispersion parameter optimization will bediscussed. As illustrated in FIG. 1, the system includes and OCT imagingsystem 100 and Offline processing 101. As further illustrated in FIG. 1,the OCT imaging system 100 may be used for data acquisition, enteringthe parameters into the software, acquiring the image and displaying theimage. The Offline Processor 101 may be used to analyze the dataacquired by the OCT system 100 and generate/optimize the parametersentered into the OCT imaging system 100. Using conventional methods, theone or more parameters typically may be determined either by trial anderror, for example, the system user may try various values until anoptimal parameter value is determined. Alternatively, offline or postprocessing 101 associated with the OCT imaging system 100 may be used tosearch for the optimal parameter values. Conventional processes arediscussed in, for example, ULTRAHIGH-RESOLUTION HIGH SPEED RETINALIMAGING USING SPECTRAL-DOMAIN OPTICAL COHERENCE TOMOGRAPHY by Teresa C.Chen (Optics Express, Volume 12, No. 11, May 31, 2004) andUltrahigh-resolution, high-speed, FOURIER DOMAIN OPTICAL COHERENCETOMOGRAPHY AND METHODS FOR DISPERSION COMPENSATION by Wojtkowski et al.(Optics Express, Volume 12, No. 11, May 31, 2004), the disclosures ofwhich are incorporated herein by reference as if set forth in theirentirety.

Two separate dispersion compensation algorithms are discussed inWojtkowski. The first involves re-scaling of raw spectrum data duringinterpolation of SDOCT data from wavelength space to wavenumber space.This algorithm may provide fast results, since it does not involvecomplex computation, but the results may not be accurate, since it onlycorrects dispersion at a single depth in the sample.

According to the second algorithm discussed in Wojtkowski, the correctedspectrum data is obtained from a Hilbert transform of the raw spectrummodified by optimized phase correction parameters. This algorithm mayprovide accurate results, since it corrects dispersion for all depths inthe sample simultaneously, but the results are not provided fast, sincethe Hilbert transform process as proposed is a computationally complexoperation involving multiple forward and inverse Fouriertransformations.

Thus, the processes discussed with respect to FIG. 1 and in the Chen andWojtkowski references may be inaccurate (providing sub-optimal images),excessively time consuming (thus possibly preventing real time systemoperation), or may require detailed information regarding the specificdispersion properties of the system optics and/or each sample to beimaged. Accordingly, improved methods of numerical dispersioncompensation may be desired.

SUMMARY OF THE INVENTION

Some embodiments of the present invention provide methods, systems andcomputer program products for generating parameters for softwaredispersion compensation in optical coherence tomography (OCT) systems.Raw spectral interferogram data is acquired for a given lateral positionon a sample and a given reference reflection. A trial spectral phasecorresponding to each wavenumber sample of the acquired spectralinterferogram data is postulated. The acquired raw spectral data and thepostulated trial spectral phase data are assembled into trial complexspectrum data. Trial A-scan data is computed by performing an inverseFourier transform on the trial complex spectrum data and taking themagnitude of a result.

In further embodiments of the present invention, the raw spectralinterferogram data may be denoted M(k). Postulating a trial phasecorresponding to each wavenumber sample of M(k) may be done according tothe following equation:φ(k)=a(k−k ₀)+b(k−k ₀)² +c(k−k ₀)³ +d(k−k ₀)⁴,wherein k₀ is the central wavenumber of a light source of the OCTsystem, and wherein a, b, c and d are parameters to be optimized andrepresent respective first, second, third, and fourth-order correctionsto be made to a phase of the acquired raw spectral interferogram data.

In still further embodiments of the present invention, assembling mayinclude assembling the acquired raw spectral interferogram data and thetrial spectral phase data into the trial complex spectrum data accordingto the following equation:S(k)=M(k)e ^(iφ(k)),wherein e is the base of natural logarithms and i is the square root of−1.

In some embodiments of the present invention, computing may includecomputing trial A-scan data by performing an inverse Fourier transformon the trial complex spectrum data and taking the magnitude of theresult according to the following equation:|s(z)|=|I.F.T.{S(k)}|,wherein the result |s(z)| is the real-valued trial A-scan representingthe depth-resolved reflectivity of the sample at a given laterallocation, blurred by dispersion mismatch between interferometer arms butcorrected by the postulated trial spectral phase.

In further embodiments of the present invention, the postulating,assembling and computing steps may be repeated to optimize the values ofa, b, c and d based on optimization of an image quality metric appliedto the trial A-scan data |s(z)|. In certain embodiments of the presentinvention, the result may be depth independent.

Still further embodiments of the present invention provide methods,systems and computer program products for generating parameters forsoftware dispersion compensation in optical coherence tomography (OCT)systems. Optimization of parameters for software dispersion compensationis initiated. The parameters are optimized in less than about 15seconds. An image is processed using the optimized parameters withoutincreasing processing time relative to the processing of an imagewithout implementing dispersion optimization parameters.

In some embodiments of the present invention, the parameters may beoptimized in from about 3.0 to about 5.0 seconds. In certain embodimentsof the present invention, an entire depth of the sample may beoptimized.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic block diagram illustrating a conventionaltechnique for dispersion parameter optimization.

FIG. 2A is a conceptual block diagram illustrating OCT imaging softwareaccording to some embodiments of the present invention.

FIG. 2B is a flowchart illustrating operations of OCT imaging methods,systems and computer program products using automatic dispersionparameter optimization according to some embodiments of the presentinvention.

FIG. 3 is a schematic diagram illustrating systems according to someembodiments of the present invention.

FIG. 4 is a schematic diagram illustrating systems according to furtherembodiments of the present invention.

FIG. 5 is a schematic diagram illustrating systems according to someembodiments of the present invention.

FIG. 6 is a flowchart illustrating operations of OCT imaging methods,systems and computer program products according to some embodiments ofthe present invention.

DETAILED DESCRIPTION OF EMBODIMENTS OF THE INVENTION

The invention now will be described more fully hereinafter withreference to the accompanying drawings, in which illustrativeembodiments of the invention are shown. This invention may, however, beembodied in many different forms and should not be construed as limitedto the embodiments set forth herein; rather, these embodiments areprovided so that this disclosure will be thorough and complete, and willfully convey the scope of the invention to those skilled in the art.Like numbers refer to like elements throughout. As used herein, the term“and/or” includes any and all combinations of one or more of theassociated listed items.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

Unless otherwise defined, all terms (including technical and scientificterms) used herein have the same meaning as commonly understood by oneof ordinary skill in the art to which this invention belongs. It will befurther understood that terms, such as those defined in commonly useddictionaries, should be interpreted as having a meaning that isconsistent with their meaning in the context of the relevant art andthis specification and should not be interpreted in an idealized oroverly formal sense unless expressly so defined herein.

As will be appreciated by one of skill in the art, the invention may beembodied as a method, data processing system, or computer programproduct. Accordingly, the present invention may take the form of anentirely hardware embodiment, an entirely software embodiment or anembodiment combining software and hardware aspects all generallyreferred to herein as a “circuit” or “module.” Furthermore, the presentinvention may take the form of a computer program product on acomputer-usable storage medium having computer-usable program codeembodied in the medium. Any suitable computer readable medium may beutilized including hard disks, CD-ROMs, optical storage devices, atransmission media such as those supporting the Internet or an intranet,or magnetic storage devices.

Computer program code for carrying out operations of the presentinvention may be written in an object oriented programming language suchas Java®, Smalltalk or C++. However, the computer program code forcarrying out operations of the present invention may also be written inconventional procedural programming languages, such as the “C”programming language or in a visually oriented programming environment,such as VisualBasic.

The program code may execute entirely on the user's computer, partly onthe user's computer, as a stand-alone software package, partly on theuser's computer and partly on a remote computer or entirely on theremote computer. In the latter scenario, the remote computer may beconnected to the user's computer through a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

The invention is described in part below with reference to a flowchartillustration and/or block diagrams of methods, systems and computerprogram products according to embodiments of the invention. It will beunderstood that each block of the illustrations, and combinations ofblocks, can be implemented by computer program instructions. Thesecomputer program instructions may be provided to a processor of ageneral purpose computer, special purpose computer, or otherprogrammable data processing apparatus to produce a machine, such thatthe instructions, which execute via the processor of the computer orother programmable data processing apparatus, create means forimplementing the functions/acts specified in the block or blocks.

These computer program instructions may also be stored in acomputer-readable memory that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablememory produce an article of manufacture including instruction meanswhich implement the function/act specified in the block or blocks.

The computer program instructions may also be loaded onto a computer orother programmable data processing apparatus to cause a series ofoperational steps to be performed on the computer or other programmableapparatus to produce a computer implemented process such that theinstructions which execute on the computer or other programmableapparatus provide steps for implementing the functions/acts specified inthe block or blocks.

Some embodiments of the present invention will now be discussed withrespect to FIGS. 1 through 6. As discussed with respect thereto, someembodiments of the present invention may provide an automatic dispersioncompensation optimization where the OCT imaging software searches forthe optimal value(s) relatively quickly when triggered by the userand/or by a condition in the software. Many optimization metrics can beused, for example, maximizing the brightest spot or line in the image orby maximizing the number of lines or spots in the image with intensitythat may exceed some specified threshold. It will be understood thatvarious computational architectures can be used to implement embodimentsof the present invention and, therefore, embodiments of the presentinvention should not be limited to the computational architecturesdescribed herein.

As discussed herein, some embodiments of the present invention providehardware and/or software systems, methods and computer program productsthat use search or optimization routines to find parameters for softwaredispersion compensation in OCT systems in real time. Various embodimentsof the present invention are described below including multiple hardwareand/or software configurations for finding and using these optimizeddispersion parameters. It will be understood that the configurationsdiscussed herein are provided for exemplary purposes only and,therefore, embodiments of the present invention should not be limitedthereby.

OCT imaging systems may typically be categorized in two generalcategories, time domain (TD)-OCT, where a moving mirror or prism in thereference arm determines the current imaging depth location in thesample, and Fourier domain (FD)-OCT, where the reference arm is fixed inlength and data is acquired over a spectrum of wavelengths to changeimaging depth location in the sample. FD-OCT is typically furthercategorized into two categories, swept source (SS)-OCT (SS-OCT) andspectral domain (SD)-OCT. For SS-OCT, a narrow-line width laser istypically swept in wavelength over time to interrogate the sample atdifferent wavelengths. For SD-OCT, a broad band (low coherence) source,such as a superluminescent diode (SLD), is typically used in conjunctionwith a spectrometer. Various embodiments of the present invention may beused with any of these hardware implementations without departing fromthe scope of the present invention.

OCT systems typically operate by acquiring depth data at a particularpoint on the sample, which may be called an A-scan. The OCT beam ismoved relative to the sample using, for example, any of the variousdepth adjustment approaches described above, and another set of depthdata may be acquired. These series of depth images are put together toform a 2-D image, which may be called a B-scan. Any scan pattern cangenerally be used, where commonly used scan patterns include, forexample, linear and circular. By scanning in two directions instead ofjust one, a 3-D volume of data can be acquired. Again any scan patterncan generally be used, where commonly used 3-D scan patterns include,for example, rectangular, sets of radial lines, and sets of concentriccircles.

OCT data is a measurement of the reflectivity at each depth in thesample at a given point. In other words, the contrast in the image isgenerally due to variations in the reflectivity in the sample. Othercontrast mechanisms may be used without departing from the scope of thepresent invention. For example, polarization contrast and spectralcontrast may be used.

A property of most OCT systems is that the image quality may be affectedby the relative dispersion of the sample and reference paths.Uncompensated dispersion typically shows up as blurring in the OCTimage. There are multiple methods that can be used to compensate fordispersion. For example, the physical properties of the sample orreference path may be changed to make the dispersion in the two armsidentical or nearly so. This technique has some drawbacks. For example,in retinal imaging the sample path includes approximately one inch ofwater in the aqueous and vitreous humor in the eye so the reference pathalso needs one inch of compensating water. Building a robust water cellmay add significant complexity and cost of the OCT system. Furthermore,when the sample path is partially in the sample, as is the case in theretina, changing from one subject to another may change the dispersionproperties of the overall system and may require different compensation.

Another technique for dispersion compensation is to numericallycompensate for the dispersion as the OCT image processing is done.Typically, in SD-OCT systems, the data from the spectrometer isre-sampled so that it is evenly spaced in wavenumber instead of beingevenly space in wavelength. When this calculation is done, thedispersion compensation may show up as higher order terms in theresampling equation. For SS-OCT systems, a resampling may or may not berequired depending on the system design, but regardless, the dispersioncompensation can typically be performed. This numerical dispersioncompensation is dependent on the accuracy of the parameters used in thenumerical compensation; inaccurate values may still result in blurredimages.

These dispersion compensation parameter(s) can be determined by avariety of techniques. For example, the user may enter a set ofparameters, look at the resulting image and change the parameters. Inother words, the user may try to get the best image by looking at theimage for various values of the dispersion parameter(s). This techniqueis typically slow, unreliable and hard to repeat. A more accuratetechnique is a take a line or frame of OCT data and run it throughanother program, which searches for the best dispersion parameters. Thisprogram is typically, but need not be, separate from the program thatacquired the raw data and transforms it into OCT images. The searchprogram can be built in a variety of ways particularly in the search andin the optimization metric. For this type of search, a metric istypically defined and the search tries to optimize the value of themetric. Any of a number of metrics can be used. In addition, the methodused to search the parameter space can be accomplished by a variety oftechniques or schemes. For example, the search may be conducted on aseparate machine from where the imaging is occurring and may takeseveral minutes to transfer the raw OCT data, run the optimizationsoftware, and transfer the dispersion compensation parameters back tothe OCT imaging program. The delay induced by this process may beundesirable.

Referring now to FIG. 2A, a conceptual block diagram illustrating OCTimaging methods, systems and computer program products according to someembodiments of the present invention will be discussed. As illustratedin FIG. 2A, the dispersion algorithm according to some embodiments ofthe present invention is integrated with the OCT imaging program,thereby potentially reducing the likelihood that an off-linedetermination of dispersion parameters will be needed. In particular,the OCT imaging software module 260 according to some embodiments of thepresent invention includes a software module that triggers optimizationof the parameters 252, a software module that actually optimizes theparameters 253, a software module that acquires an image 250 after theparameters are loaded 254 and a software module that processes anddisplays the images 251. Many of these steps may be performed using analgorithm. Exemplary algorithms according to some embodiments of thepresent invention will be discussed below with respect to the flowchartof FIG. 6.

Operations of OCT imaging systems according to some embodiments of thepresent invention will now be discussed with respect to the flowchart ofFIG. 2B. As discussed above, OCT imaging software is configured toprovide dispersion parameter optimization. In particular, operations ofthe OCT imaging software according to some embodiments of the presentinvention begin at block 200 by triggering optimization. Theoptimization may be triggered by, for example, the user, by somecondition in the software, such as for each acquisition line, eachframe, each volume, once a day, once an hour, once an image set and thelike and/or a combination of thereof. Once the optimization istriggered, the software switches from image acquisition and display andthe parameters may be optimized (block 210). It typically takes fromabout a few seconds to about a few tens of seconds to find the optimaldispersion compensation parameters with typical commercial centralprocessing units (CPUs), though this speed may be, in principle, reducedto beneath a single line acquisition time frame. Once the parameters areoptimized, the parameters may be transferred to the imaging portion ofthe system, where the image may be acquired (block 220) and displayed(block 230).

When the optimization is triggered, the software takes some of therecently acquired data and uses it to optimize the dispersionparameters. This may be accomplished by starting with a set ofparameters, calculating an image, measuring some metric regarding atleast some portion of the image, adjusting the set of parameters, andrepeating. The goal is to maximize (or minimize) the metric whilesearching through the parameter space. As used herein, “maximize” and“minimize” refer to getting close to the maximum and/or minimum, but notnecessarily achieving an absolute maximum and/or minimum. One metricthat can be used includes finding the brightest spot or line in theimage and making it as bright as possible. Another metric that may beused is to maximize the number of lines or spots that are above somefixed or variable threshold level. Still another metric that may be usedis to maximize another aspect of the intensity distribution function.

The remaining portion of the software routine is how the parameter spaceis searched to optimize the given metric. Any number of searchalgorithms may work and, therefore, embodiments of the present inventionare not limited to the algorithms described herein. For example, a setstep search may be conducted one parameter at a time. The mostsignificant parameter may be searched first. The search starts at aspecified number and takes steps of a set size until the metric passesthrough a maximum (or minimum). Then the search switches to a binarysearch where the two points around the maximum are used as the startingpoint and then a point half way in between is checked. The midpoint andthe highest end point become the starting values for the next iteration.Once the most significant parameter is optimized, the next parameter isoptimized using the same technique. This may work for any number ofparameters.

According to further embodiments of the present invention, a strictbinary search may be used for all parameters by setting an initial upperand lower limit on the parameter value and searching within that spacefor the optimal value. According to still further embodiments of thepresent invention, parameters may be iteratively reoptimized as eachparameter is adjusted. For example if there are two parameters, A and B;A would be optimized, B would be optimized, then A would be reoptimizedbased on the new value of B and B would be reoptimized based on the newvalue of A. This can be done for a set number of iterations or untilsome metric is satisfied, such as the change in A and B after areoptimization is less than a given level.

In general, dispersion parameters consist of an ordered set ofsequentially higher order terms. The lowest order term is substantiallyequivalent to the refractive index of the material, and impacts thelength scaling of the image. The next three higher order terms are thefirst, second, and third derivatives of the refractive index withrespect to frequency (or wavelength). Implementations of the dispersionoptimization algorithm may include optimizing one, two, or three ofthese terms. Further, the optimization may include sequentiallyoptimizing each ordered term independently, searching for the nexthigher order term only after the preceding term has been optimized.Alternatively, a multi-dimensional global optimization routine may bedeployed, although this may be computationally costly and may be withoutadded benefit. Generally, use of a fourth order or higher term is notrequired, but can be performed without departing from the scope of thepresent invention.

Some embodiments of the present invention will now be discussed withreference to the schematic block diagram of FIG. 3. As illustrated inFIG. 3, a computer 300 running software is connected to an OCT imagingsystem 301. The computer executes software that may be resident inmemory. Various software modules in the memory may, among other things,process raw data to create OCT image datasets, for example, A-scans,B-scans and/or volume images, display data to a user monitor (or thelike), store data, and optimize various parameters for dispersioncompensation or other affects.

Further embodiments of the present invention will now be discussed withrespect to FIG. 4. As illustrated in FIG. 4, the computer 410 runningsoftware interfaces with an OCT imaging system 411 that includesinternal processing capability 412. This processor may be, for example,a microprocessor, a digital signal processor (DSP), a field programmablegate array (FPGA), some combination of the these or the like withoutdeparting from the scope of the present invention. This system stillperforms the tasks of processing raw data, displaying data to a usermonitor, storing and retrieving data, and optimizing various parametersfor dispersion compensation or other affects, but the tasks are splitbetween the processor internal to the OCT imaging system 411 and thecomputer 410. The tasks can be split in any fashion and may change overtime. One possible configuration may be, for example, having theinternal processor 412 perform the processing of the raw data and theoptimization of the parameters while the computer 410 displays the datato the user monitor and stores and retrieves data.

Some embodiments of the present invention will now be discussed withrespect to FIG. 5. As illustrated therein, the internal processor 521and the display 522 are provided in the OCT imaging system 520. Asdiscussed above, the processor may be a microprocessor, a DSP, an FPGA,some combination of the these or other processor without departing fromthe scope of the present invention. According to embodiments of thepresent invention illustrated in FIG. 5, all tasks may now be performedwithin the OCT imaging system including processing raw data to createOCT image datasets, for example, A-scans, B-scans and/or volume images,displaying data to a user monitor (or the like), storing and retrievingdata, and optimizing various parameters for dispersion compensation orother affects.

Referring now to FIG. 6, a flowchart illustrating algorithms fornumerical compensation of dispersion mismatch between the sample andreference arms in Fourier domain optical coherence tomography (FDOCT)systems according to some embodiments of the present invention will bediscussed. The FDOCT systems may include spectrometer-based spectraldomain optical coherence tomography (SDOCT) and swept-source opticalcoherence tomography (SSOCT) variants without departing from the scopeof the present invention. The algorithm discussed herein according tosome embodiments of the present invention may provide advantages overconventional methods, for example, the methods discussed in Chen andWojtkowski, et. al, the disclosures of which have been previouslyincorporated herein by reference. For example, use of the algorithmaccording to some embodiments of the present invention may provideresults fast enough for real-time computation and accurate enoughresults to correct dispersion for all depths in the sample being imaged.

General methods, systems and computer program products for automatednumerical dispersion compensation in FDOCT have been discussed abovewith respect to FIG. 1 through 5. As discussed with respect thereto,automated numerical dispersion compensation in FDOCT may be based uponthe optimization of one or more parameters inherent to digital signalprocessing of FDOCT datasets, which may be based upon optimization of apredetermined image quality metric.

The algorithm illustrated by the flowchart of FIG. 6 illustratesspecific embodiments the general methods, systems and computer programproducts discussed above with respect to FIGS. 1 through 5. Inparticular, operations begin at block 600 by acquiring raw spectralinterferogram data, denoted M(k), for a given lateral position on asample and a given reference reflection. The variable k representswavenumber, which is related to optical wavelength according to therelation k=2π/λ, where λ is wavelength. The data M(k) represents samplesof a continuous real function stored in a computer. In some embodimentsof the present invention, the number of samples is a power of 2, such as1024 or 4096. Furthermore, the spectral interferogram samples may beevenly distributed in k.

As discussed above, some embodiments of the present invention may beused in both SDOCT and SSOCT systems. In SDOCT systems according to someembodiments of the present invention, the data M(k) may be acquired froma spectrometer illuminating a CCD line camera, in which case numericalinterpolation may be required to re-sample the original spectral data(which may be evenly sampled in λ in the case of grating-basedspectrometers) to be evenly sampled in k. In SSOCT systems according tosome embodiments of the present invention, the data M(k) may be directlysampled from the output of a single-channel optical receiver.

Operations according to some embodiments of the present inventioncontinue at block 610 by postulating a trial phase corresponding to eachwavenumber sample of M(k) according to the following equation:φ(k)=a(k−k ₀)+b(k−k ₀)² +c(k−k ₀)³ +d(k−k ₀)⁴+   Equation (1)In these embodiments of the present invention, k₀ is the centralwavenumber of the light source used in the FDOCT system, and thevariables a, b, c, d . . . are the parameters to be optimized to improveimage quality. The variables a, b, c, d represent first, second, third,and fourth-order corrections to be made to the phase of the spectralinterferogram data. As used in the equation set out above, the ellipsis( . . . ) indicates that more or less orders may be needed in particularsituations to achieve a desired image quality result according to someembodiments of the present invention. Typically, at least third-ordercorrection is required for suitable image quality, and rarely are morethan ten orders of correction required.

Operations continue at block 620 by assembling the raw spectral data andthe trial spectral phase data into the trial complex spectrum dataaccording to the following equation:S(k)=M(k)e ^(iφ(k)).   Equation (2)In these embodiments, e is the base of natural logarithms and i is thesquare root of −1. Operations according to some embodiments of thepresent invention continue at block 630 by computing trial A-scan databy performing an inverse Fourier transform on the trial complex spectrumdata and determining the magnitude of the result, according to thefollowing equation:|s(z)|=|I.F.T.{S(k)}|.   Equation (3)The result |s(z)| is the real-valued trial A-scan representing thedepth-resolved reflectivity of the sample at a given lateral location,blurred by dispersion mismatch between the interferometer arms butcorrected by the trial phase postulated in the first step. Asillustrated in FIG. 6, operations of blocks 710 through 730 may berepeated to optimize the values for the variables a, b, c, d . . . basedon optimization of an image quality metric applied to the trial A-scandata |s(z)|.

It will be understood that a number of parameter optimization techniquesmay be used to search for the optimal parameters without departing fromthe scope of the present invention. For example, the value of thefirst-order parameter a may be optimized by maximizing the image qualitymetric to some desired accuracy. Then, the value of the second-orderparameter b may be optimized in a similar manner, and so on. Asdiscussed above, useful image quality metrics may include the maximumpeak value observed in the trial A-scan data |s(z)|, the number of peaksabove a specified threshold data, or the narrowness of an individualreflectance peak in |s(z)| arising from a well-defined reflection in atrial sample.

The optimization procedure just described may be applied to individualA-scans, or may alternatively be applied to an entire multidimensionalimage or volume dataset consisting of many individual A-scans. In theseembodiments of the present invention, the same trial phase parameters a,b, c, d . . . are applied for each A-scan in the multidimensionaldataset during the optimization procedure, and the image quality metricthen corresponds to two (or higher) dimensional metrics, such as thehighest peak in a two-dimensional image, the number of peaks above aspecified threshold in a two-dimensional image, or the narrowness of anindividual reflectance peak in two dimensions.

Once found through the optimization procedure, the optimized parametersa, b, c, d . . . may then be used for fast, real-time processing of eachsubsequent A-scan acquired for a given imaging geometry according tosome embodiments of the present invention. As discussed above, blocks610 through 630 of FIG. 6 illustrate the steps of the fast dispersioncorrection algorithm according to some embodiments of the presentinvention. The optimized parameters a, b, c, d . . . may be used inplace of trial parameters. These parameters will be applicable fornumerical dispersion compensation until the amount of dispersion ineither the sample or reference arm changes appreciably.

As discussed above, some embodiments of the present invention mayprovide advantages over conventional methods. For example, unlikeconventional methods, the algorithm according to some embodiments of thepresent invention may correct dispersion for all depths in the samplesimultaneously, so long as the spectral data is calibrated accurately,and may operate in real time.

The foregoing is illustrative of the present invention and is not to beconstrued as limiting thereof. Although a few exemplary embodiments ofthis invention have been described, those skilled in the art willreadily appreciate that many modifications are possible in the exemplaryembodiments without materially departing from the novel teachings andadvantages of this invention. Accordingly, all such modifications areintended to be included within the scope of this invention as defined inthe claims. Therefore, it is to be understood that the foregoing isillustrative of the present invention and is not to be construed aslimited to the specific embodiments disclosed, and that modifications tothe disclosed embodiments, as well as other embodiments, are intended tobe included within the scope of the appended claims. The invention isdefined by the following claims, with equivalents of the claims to beincluded therein.

1. A method for generating parameters for software dispersioncompensation in optical coherence tomography (OCT) systems, comprising:acquiring raw spectral interferogram data for a given lateral positionon a sample and a given reference reflection; postulating a trialspectral phase corresponding to each wavenumber sample of the acquiredspectral interferogram data; assembling the acquired raw spectral dataand the postulated trial spectral phase data into trial complex spectrumdata; and computing trial A-scan data by performing an inverse Fouriertransform on the trial complex spectrum data and determining themagnitude of a result.
 2. The method of claim 1: wherein the rawspectral interferogram data is denoted M(k); and wherein postulatingcomprises postulating a trial phase corresponding to each wavenumbersample of M(k) according to the following equation:φ(k)=a(k−k ₀)+b(k−k ₀)² +c(k−k ₀)³ +d(k−k ₀)⁴, wherein k₀ is the centralwavenumber of a light source of the OCT system, and wherein a, b, c andd are parameters to be optimized and represent respective first, second,third, and fourth-order corrections to be made to a phase of theacquired raw spectral interferogram data.
 3. The method of claim 2,wherein assembling comprises assembling the acquired raw spectralinterferogram data and the trial spectral phase data into the trialcomplex spectrum data according to the following equation:S(k)=M(k)e ^(iφ(k)), wherein e is the base of natural logarithms and iis the square root of −1.
 4. The method of claim 3, wherein computingcomprises computing trial A-scan data by performing an inverse Fouriertransform on the trial complex spectrum data and determining themagnitude of the result according to the following equation:|s(z)|=|I.F.T.{S(k)}|, wherein the result |s(z)| is the real-valuedtrial A-scan representing the depth-resolved reflectivity of the sampleat a given lateral location, blurred by dispersion mismatch betweeninterferometer arms but corrected by the postulated trial spectralphase.
 5. The method of claim 4, further comprising repeating thepostulating, assembling and computing steps to optimize the values of a,b, c and d based on optimization of an image quality metric applied tothe trial A-scan data |s(z)|.
 6. The method of claim 1, wherein theresult is depth independent.
 7. A method for generating parameters forsoftware dispersion compensation in optical coherence tomography (OCT)systems, comprising: initiating optimization of parameters for softwaredispersion compensation; optimizing the parameters in less than about 15seconds; and acquiring an image using the optimized parameters withoutincreasing processing time.
 8. The method of claim 7, wherein optimizingthe parameters comprises optimizing the parameters in from about 3.0 toabout 5.0 seconds.
 9. The method of claim 7, wherein optimizing furthercomprises optimizing through an entire depth of the sample.
 10. Anoptical coherence tomography (OCT) system, comprising: an imaging systemwith automatic parameter optimization, the imaging system beingconfigured to: acquire raw spectral interferogram data for a givenlateral position on a sample and a given reference reflection; postulatea trial spectral phase corresponding to each wavenumber sample of theacquired spectral interferogram data; assemble the acquired raw spectraldata and the postulated trial spectral phase data into trial complexspectrum data; and compute trial A-scan data by performing an inverseFourier transform on the trial complex spectrum data and determining themagnitude of a result.
 11. The system of claim 10, wherein the rawspectral interferogram data is denoted M(k), the imaging system beingfurther configured to postulate a trial phase corresponding to eachwavenumber sample of M(k) according to the following equation:φ(k)=a(k−k ₀)+b(k−k ₀)² +c(k−k ₀)³ +d(k−k ₀)⁴, wherein k₀ is the centralwavenumber of a light source of the OCT system, and wherein a, b, c andd are parameters to be optimized and represent respective first, second,third, and fourth-order corrections to be made to a phase of theacquired raw spectral interferogram data.
 12. The system of claim 11,wherein the imaging system is further configured to assemble theacquired raw spectral interferogram data and the trial spectral phasedata into the trial complex spectrum data according to the followingequation:S(k)=M(k)e ^(iφ(k)), wherein e is the base of natural logarithms and iis the square root of −1.
 13. The system of claim 12, wherein theimaging system is further configured to compute trial A-scan data byperforming an inverse Fourier transform on the trial complex spectrumdata and determining the magnitude of the result according to thefollowing equation:|s(z)|=|I.F.T.{S(k)}|, wherein the result |s(z)| is the real-valuedtrial A-scan representing the depth-resolved reflectivity of the sampleat a given lateral location, blurred by dispersion mismatch betweeninterferometer arms but corrected by the postulated trial spectralphase.
 14. The system of claim 13, wherein the imaging system isconfigured to repeatedly postulate, assemble and compute steps tooptimize the values of a, b, c and d based on optimization of an imagequality metric applied to the trial A-scan data |s(z)|.
 15. The systemof claim 10, wherein the result is depth independent.
 16. An opticalcoherence tomography (OCT) system, comprising: an imaging system withautomatic parameter optimization, the imaging system being configuredto: initiate optimization of parameters for software dispersioncompensation; optimize the parameters in less than about 15 seconds; andacquire an image using the optimized parameters without increasingprocessing time.
 17. The system of claim 16, wherein the imaging systemis further configured to optimize the parameters in from about 3.0 toabout 5.0 seconds.
 18. The system of claim 16, wherein the imagingsystem is configured to optimize through an entire depth of the sample.19. A computer program product for generating parameters for softwaredispersion compensation in optical coherence tomography (OCT) systems,the computer program product comprising: computer readable storagemedium having computer readable program code embodied in the medium, thecomputer readable program code comprising: computer readable programcode configured to acquire raw spectral interferogram data for a givenlateral position on a sample and a given reference reflection; computerreadable program code configured to postulate a trial spectral phasecorresponding to each wavenumber sample of the acquired spectralinterferogram data; computer readable program code configured toassemble the acquired raw spectral data and the postulated trialspectral phase data into trial complex spectrum data; and computerreadable program code configured to compute trial A-scan data byperforming an inverse Fourier transform on the trial complex spectrumdata and determining the magnitude of a result.
 20. The computer programproduct of claim 19, wherein the raw spectral interferogram data isdenoted M(k), the computer program product further comprising computerreadable program code configured to postulate a trial phasecorresponding to each wavenumber sample of M(k) according to thefollowing equation:φ(k)=a(k−k ₀)+b(k−k ₀)² +c(k−k ₀)³ +d(k−k ₀)⁴, wherein k₀ is the centralwavenumber of a light source of the OCT system, and wherein a, b, c andd are parameters to be optimized and represent respective first, second,third, and fourth-order corrections to be made to a phase of theacquired raw spectral interferogram data.
 21. The computer programproduct of claim 20, further comprising computer readable program codeconfigured to assemble the acquired raw spectral interferogram data andthe trial spectral phase data into the trial complex spectrum dataaccording to the following equation:S(k)=M(k)e ^(iφ(k)), wherein e is the base of natural logarithms and iis the square root of −1.
 22. The computer program product of claim 21,further comprising computer readable program code configured to computetrial A-scan data by performing an inverse Fourier transform on thetrial complex spectrum data and determining the magnitude of the resultaccording to the following equation:|s(z)|=|I.F.T.{S(k)}|, wherein the result |s(z)| is the real-valuedtrial A-scan representing the depth-resolved reflectivity of the sampleat a given lateral location, blurred by dispersion mismatch betweeninterferometer arms but corrected by the postulated trial spectralphase.
 23. The computer program product of claim 22, further comprisingcomputer readable program code configured to repeat the postulating,assembling and computing steps to optimize the values of a, b, c and dbased on optimization of an image quality metric applied to the trialA-scan data |s(z)|.
 24. The computer program product of claim 19,wherein the result is depth independent.
 25. A computer program productfor generating parameters for software dispersion compensation inoptical coherence tomography (OCT) systems, the computer program productcomprising: computer readable program code configured to initiateoptimization of parameters for software dispersion compensation;computer readable program code configured to optimize the parameters inless than about 15 seconds; and computer readable program codeconfigured to acquire an image using the optimized parameters withoutincreasing processing time.
 26. The computer program product of claim25, further comprising computer readable program code configured tooptimize the parameters in from about 3.0 to about 5.0 seconds.
 27. Thecomputer program product of claim 25, further comprising computerreadable program code configured to optimize through an entire depth ofthe sample.